Now my bankroll is
$517.36 from $1678.98 or down 71% and I am 2 – 3 for the week.

While the chance of
this exact outcome (losing the first three of my picks, and getting
the last one) is very unlikely, the probability of only getting one
pick right has a low probability, but as unlikely as you may think.
Let me explain.

Calculating the
probability of events that can only have two outcomes in this case is
my pick right or wrong is called binomial. To calculate, the
probability that a particular event will happen, in this case of me
getting one pick right, you have to add together all the scenarios
that causes an event to happen.

So, one of the
scenarios of getting one pick right was what happened yesterday, the
first three picks were wrong and the last pick was right which is
represented by the set, {lllw}.

Where the “l”
represents a lose on my part for that particular pick and “w” is
a win. So you can read {lllw} as picks 1, 2, and 3 are losses and
pick 4 is win. For the event, 3 loses and 1 win, there are four
scenarios that can cause that event the one given above and three
more: {llwl},{lwll}, and {wlll}. The probability of a 1 – 3 pick
day for the probabilities from yesterday's picks would be as follows.

The probability of
{lllw} written in shorthand p({lllw}) given my probability estimates:
game 1 UI -3.5 vs Maryland 71.43%, game 2 Purdue +8.5 vs Iowa 71.43%,
game 3 South Carolina -5.5 vs Missouri 64.00%, and Tennessee +17.5 at
Georgia 60.47% would be

While these are
probabilities for week 5 of the College Football 2014-2015 season, my
pick probabilities for the 4 games are about the same every week
(between 60% – 70%), so you can expect any given Saturday that
there will be a 10% chance of only getting one pick right (in other words it is most likely to happen a few times a season) and for the
dire probability of picking all the games wrong at 1%, but on the flip side I have a 99% of not loses all my money in one week.

So, I will, literally, probably make the money back, the probability of picking 3 or more games correctly is about 60% (or the sum of getting 4 picks correct, ~20%, and getting 3 picks correct, ~40%, on a any given Saturday). In the long run, I will get my money back and it will keep growing.

Saturday, September 27, 2014

This week I added a statistics page which are the statistics I use
for my picks.

Initially, this season I used stats from the Sunshine Forcast, but the page owner Walter Repole has not updated his
projections or stats in a while. Last Sunday, I decided to write an
program to collect my own stats and spreads. To collect the data, I
wrote a computer program called a web crawler, a web crawler
traverses a web site and retrieves information, I wrote one that gets
publicly available stats from espn.com and the spreads are from www.bettorsworld.com.

In the past seasons, I used stats from CFBStats
which used to be free to access current week stats and archived
stats, but the site began charging for them this season under the
domain http://coachesbythenumbers.com.
CFBStats had tons of stats from not only team game performance but
individual drive and play-by-play stats as well as stuff like game
attendance, and it looks like the pay site has the same data. The packages range from $500/yr to $2000/yr based on how
in depth you want the statistics. They had tons of stats so I do not
blame them for charging. They did the work, and they should be
compensated. My statistics are not as in-depth and only include basic
statistics you can get from a regular box score.

I will add stats for additional sports throughout the year.

If you
have any other data you would like to see list them in the comments.

Of my picks for record for the 2014-2015 NCAA college football
season that with week 4, I have gone 4-2 over the spread and with my
starting bankroll of $1000 increasing 68% percent to $1678.98.

Dr. Wag's Lock of
the Week

Indiana -3.5 vs
Maryland with a 71.43% change of being correct and calculated 40%
Kelly criterion

Indiana's strong
offense has a mismatch against a relatively weak Maryland offense and
this game gives the best value. The rest of the top picks are all
have strong offensive mismatches and have a high probability of
beating the spread.

Other Picks for
Record

Purdue +8.5 vs Iowa
also with a 71.43% probability of being correct and 40% Kelly bet

South Carolina -5.5
vs Missouri with a 64.00% probability of being correct and a 24%
Kelly bet

Tennessee +17.5 at
Georgia with a 60.47% probability of being correct and a 17% Kelly
bet

Based on my own
betting rules, I will not take put more than 30% of the bankroll on a
given game so my bets are as follows with a $1678.98 starting
bankroll:

$510 (30% of
bankroll) on Indiana -3.5 against Maryland

$510 (30% of
bankroll) on Purdue +8.5 against Iowa

$407 (24% of
bankroll) on South Carolina -5.5 against Missouri

$271 (16% of
bankroll, rest of bankroll) on Tennessee +17.5 at Georgia

Again, I'm betting
my whole bankroll but the probability of getting all the picks wrong
is low, about 1% (I got the probability from the equation
(1-0.7143)X(1-0.7143)X(1-.64)X(1-.6047) or multiplying all the one
minus all the probabilities together).

Picks for 09/27/2014

My algorithm adjusted for the spread increase of the UNLV vs San Diego State game from UNLV -17.5 to -18.5 and thought that was too high, so the Maryland Indiana game moved to the top spot. On the top pretty much every other game stayed the same.

Thursday, September 25, 2014

My bet for tonight's
UCLA vs ASU game was -4.0 for UCLA, but the UCLA crushed ASU by 35 points
and easily covered the spread.

$320
(Kelly 30%) on UCLA -4.0 against ASU Win UPDATED $290.90

I started with
$1069.08 today and I will go into Saturday's picks with $1389.98.

Update 10/17/2014: put my bankroll $1689.98 on a previous post. It is a miscalculation caught by jiceman on Reddit.com, but I don't want to go back and 3 weeks worth of values, so let's just say I won an extra 320 units playing craps and I'm carrying $1689.98 into the next period.

My bet for September 25th starting with $1069.08 bankroll based on what I had on Saturday.

$320 (Kelly 30%) on UCLA -4.0 against ASU

UCLA has had an 68% probability of beating the spread for the last two days based on my algorithm, and per my empirical testing 30% max bet of bankroll produced the best results. If you want to read to read more about Kelly betting you should read my post of the topic.

Wednesday, September 24, 2014

Picks for 09/25/2014

Today is the first time the UCLA vs Arizona State Game has come up on the algorithm picks as a good bet.

Team

Spread

Prob Winner

Kelly

UCLA

-4.0

68.18%

33%

Arizona State

+4.0

Picks for 09/27/2014

Baylor vs Iowa State shots to the top of today's picks. According to my software this is the top pick. Baylor has been on fire this season, but I would be a little hesitant about a bet with such a large spread, but that is why I use computer models for betting because my gut is often wrong.

If I had done even bets on the all the game ($200), I would have a bankroll of $1145.45. While I am lower than if I was even betting, in the long run I will be ahead of even betting with Kelly betting.

On this blog, I am also going to record a hypothetical betting for this college football season. The rules for the betting are:

I will start the season with a bankroll of $1000 on Sept 19th, 2014, I know this is a day late

There will be no maximum to the amount I can bet

The minimum bet amount is $5

If the bankroll goes below $100, it plus up back to $100

The
following day bankroll will be the based on the amount of the bet from
winnings and loses from the previous day, so even though I could have a
compounded bets multiple times on Saturdays due to the stagger of game
times, I will consider all games starting at the same time for
simplicity

All bets will be in whole dollar amount; where $100 would be allowed but not $100.49

I will cap the Kelly bet to 30% of the bank roll, I do this for a few of reasons

1. I’m a conservative better and the idea of betting 60% or 70% on one game makes me squeamish

2.This allows me to have at least four bets a day therefore hedging my bets a little more

3.My testing on previous season shows this was a good number for maximum winning

I
will do no parlays, as all explain on later blogs parlays are not good
bets individual game betting has an overall better pay back

Based on this criteria my betting for Sept 19th would have been:

$200
on UConn or 20% of my bankroll based on my picks. UConn lost 14 to 17
to South Florida the spread was UConn +2.5, UConn could not cover the
spread and I lost my bet. I will have $800 to bet on Sept 20th

My bets for Sept 20th are based on my algorithmic picks will $800 in the bankroll:

$240 (Kelly 30%) on Utah State +2.0 against Arkansas State

$240 (Kelly 30%) on Mississippi State +9.0 against LSU

$216 (Kelly 27%) on Utah +3.5 against Michigan

$104 (rest of the bankroll) on Maryland + 2.5 against Syracuse

It
might be considered risky to bet the whole bankroll, but statistically
if my algorithms estimates are correct there is a very small chance I
will lose all the bets. The probability of losing all the bets are

(1-0.8333) X (1-0.6667)X(1-0.6531)X(1-0.6190) = 0.0073

So, I only have a 0.7% of losing all the bets. This formula is similar to a binomial distribution where I would have 4 outcomes where none of them are true, but the value of p is different for each pick so the above equation work.

While I believe that algorithm works based on testing with real data and Monte Carlo testing on simulated outcomes, even if my predictions were totally wrong and the picks are totally by chance the probability of all four being wrong is 6.25% or 0.50 X 0.50 X 0.50 X 0.50 where random picks have a 50% of being correct.

I get my data from the Sunshine Forecast, and this weeks stats were not updated so my confidence in my predictions is not as high as I would like.

The Kelly criterion
a betting optimization formula that tells you what percentage of your
bankroll you should bet on a particular wager based on the odds and
what probability you estimate you have of winning. The simple formula
is

f* = (bp - q)/b

where f* is percent
you should wager, b is the odds, p is the probability that the wager
is correct and q is the probability the wager is wrong or 1-p.

For most American
football and basketball bets for the spread or over/under the odds
are 100/110.

For example you want
to bet on college football game where you estimate that the
probability of beating the spread is 60% (or .6), based on the Kelly
criterion you should bet 16% of bankroll.

If the value of f*
is less than 0.0 you should not take that bet. For example, you
estimate you have a 52% percent change of getting the bet right the
value of f* is -0.008. For football and basketball you need to have a
probability of .525 or better in either direction to have a positive
Kelly bet.

Despite my PhD (or
because of it), I did most of my research for this topic on Wikipedia, but there is a good technical paper about by Jane Hung at Washington U.

There
are lots of articles for and against using the Kelly criterion I have
found it works quite well with my prediction algorithm where I
modeled 9 college football seasons using Kelly bets with a few
modifications (basically, I capped the percentage of bankroll at 30%)
and my model compounding the winnings,
in 8 seasons my model would have won money
with average increase over starting bankroll of 40,054.23% (sic).
This increase is really high a small sample due to an exceptional
year for 2013 model where the model had me winning 22,932.89% of my
payroll. If you remove that year there was still a 2140.175%
increase over the initial bankroll.

My name is Greg
Wagner, the purpose of this blog is record my betting predictions and
to discuss how I made the picks. Initially this blog will only
include college football, but I hope to add NFL, college and NBA
basketball, baseball, and soccer in that order.

I have always loved
sports and I was actively involved with football as a player and
later as referee from when I was in middle school until I went to
grad school. In grad school I studied computer science eventually
graduating with a MS and PhD in computer science from Texas Tech
University. My research was in computer vision. You can read more
about my thoughts on artificial intelligence and computer science on
my sister blog, drwag.blogspot.com.

Currently I teach computer science classes at
Pima Community College and the University of Arizona, as well as work
as a researcher in industry.

Over the last 2
years I have been working on a computer program that I could use to
predict college football games. Based on using real data and based on
Monte Carlo testing, I believe the prediction algorithm will work
over the long run. This blog will document my picks.